Patient Adoption and Utilization of a Web-Based and Mobile-Based Portal for Collecting Outcomes After Elective Orthopedic Surgery

Bell, Kevin; Warnick, Eugene; Nicholson, Kristen; Ulcoq, Sarah; Kim, Seong‐Jin; Schroeder, Gregory D.; Vaccaro, Alexander R.

doi:10.1177/1062860618765083

Cited by 19 publications

(13 citation statements)

References 22 publications

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“…In terms of accessing ePHRs using a smartphone app, our findings are consistent with Bell et al’s [ 33 ] findings that indicate accessing ePHRs through a mobile app to be associated with higher ePHR use. However, a conflicting finding was found in 2 previous studies: using the ePHRs only via a mobile device was related to infrequent use of ePHRs [ 34 , 35 ].…”

Section: Discussionsupporting

confidence: 91%

Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis

2021

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Background An electronic personal health record (ePHR), also known as a personal health record (PHR), has been broadly defined as an electronic application through which individuals can access, manage, and share their health information in a secure and confidential environment. Although ePHRs can benefit individuals as well as caregivers and health care providers, the use of ePHRs among individuals continues to remain low. Objective The current study aims to examine the relationship between human-technology interaction factors and ePHR use among adults and then to compare the different effects of human-technology interaction factors on ePHR use between younger adults (18-54 years old) and older adults (55 years of age and over). Methods We analyzed data from the Health Information National Trends Survey (HINTS 5 cycle 3) collected from US adults aged 18 years old and over in 2019. Descriptive analysis was conducted for all variables and each item of ePHR use. Bivariate tests (Pearson correlation coefficient for categorical variable and F test for continuous variables) were conducted over 2 age groups. Finally, after adjustments were made for sociodemographics and health care resources, a weighted multiple linear regression was conducted to examine the relationship between human-technology interaction factors and ePHR use. Results The final sample size of 1363 (average age 51.19) was divided into 2 age groups: 18 to 54 years old and 55 years old and older. The average level of ePHR use was low (mean 2.76, range 0-8). There was no significant difference in average ePHR use between the 2 age groups. Including clinical notes was positively related to ePHR use in both groups: 18 to 54 years old (β=.28, P=.005), 55 years old and older (β=.15, P=.006). Although accessing ePHRs using a smartphone app was only associated with ePHR use among younger adults (β=.29; P<.001), ease of understanding health information in ePHRs was positively linked to ePHR use only among older adults (β=.13; P=.003). Conclusions This study found that including clinical notes was positively related to ePHR use in both age groups, which suggested that including clinical notes as a part of ePHRs might improve the effective use of ePHRs among patients. Moreover, accessing ePHRs using a smartphone app was associated with higher ePHR use among younger adults while ease of understanding health information in ePHRs was linked to higher ePHR use among older adults. The design of ePHRs should provide the option of being accessible through mobile devices to promote greater ePHR use among young people. For older adults, providers could add additional notes to explain the health information recorded in the ePHRs.

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Section: Discussionsupporting

confidence: 91%

Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis

2021

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“…While Henry and colleagues [46] defined use as registration versus not, Arcury et al [47] and Graetz et al [86] analyzed use as a patient-reported binary metric, highlighting the variability in this metric's composition. The many studies stratifying use based on at least one login could be capturing the login required to create the account and not portal utilization as proxy for health care engagement [6,29,[48][49][50]. Some of these studies created specific classifications for users, including "nonusers," "readers," and "readers and writers" [51], potentially to mitigate their definition of use.…”

Section: Resultsmentioning

confidence: 99%

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

Beal¹,

Kolman²,

Jones³

et al. 2021

J Med Internet Res

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Background Use of patient portals has been associated with positive outcomes in patient engagement and satisfaction. Portal studies have also connected portal use, as well as the nature of users’ interactions with portals, and the contents of their generated data to meaningful cost and quality outcomes. Incentive programs in the United States have encouraged uptake of health information technology, including patient portals, by setting standards for meaningful use of such technology. However, despite widespread interest in patient portal use and adoption, studies on patient portals differ in actual metrics used to operationalize and track utilization, leading to unsystematic and incommensurable characterizations of use. No known review has systematically assessed the measurements used to investigate patient portal utilization. Objective The objective of this study was to apply systematic review criteria to identify and compare methods for quantifying and reporting patient portal use. Methods Original studies with quantifiable metrics of portal use published in English between 2014 and the search date of October 17, 2018, were obtained from PubMed using the Medical Subject Heading term “Patient Portals” and related keyword searches. The first search round included full text review of all results to confirm a priori data charting elements of interest and suggest additional categories inductively; this round was supplemented by the retrieval of works cited in systematic reviews (based on title screening of all citations). An additional search round included broader keywords identified during the full-text review of the first round. Second round results were screened at abstract level for inclusion and confirmed by at least two raters. Included studies were analyzed for metrics related to basic use/adoption, frequency of use, duration metrics, intensity of use, and stratification of users into “super user” or high utilizers. Additional categories related to provider (including care team/administrative) use of the portal were identified inductively. Additional analyses included metrics aligned with meaningful use stage 2 (MU-2) categories employed by the US Centers for Medicare and Medicaid Services and the association between the number of portal metrics examined and the number of citations and the journal impact factor. Results Of 315 distinct search results, 87 met the inclusion criteria. Of the a priori metrics, plus provider use, most studies included either three (26 studies, 30%) or four (23 studies, 26%) metrics. Nine studies (10%) only reported the patient use/adoption metric and only one study (1%) reported all six metrics. Of the US-based studies (n=76), 18 (24%) were explicitly motivated by MU-2 compliance; 40 studies (53%) at least mentioned these incentives, but only 6 studies (8%) presented metrics from which compliance rates could be inferred. Finally, the number of metrics examined was not associated with either the number of citations or the publishing journal’s impact factor. Conclusions Portal utilization measures in the research literature can fall below established standards for “meaningful” or they can substantively exceed those standards in the type and number of utilization properties measured. Understanding how patient portal use has been defined and operationalized may encourage more consistent, well-defined, and perhaps more meaningful standards for utilization, informing future portal development.

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“…This prompted the development of the KOOS JR., a more efficient 7-item survey that retained questions from the long form KOOS most relevant to patients with end-stage osteoarthritis undergoing TKA [27] . The KOOS JR., used in this study, was administered to patients via an EPRA, the use of which is associated with better patient completion rates, improved data accuracy, and cost-effectiveness relative to traditional in-office methods [28] .…”

Section: Discussionmentioning

confidence: 99%

Payer type does not impact patient-reported outcomes after primary total knee arthroplasty

et al. 2019

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Background There is a paucity of literature assessing whether payer type has an impact on postoperative patient-reported outcomes (PROs) after total knee arthroplasty (TKA). The aim of this study was to comparatively evaluate TKA PROs among patients with commercial and Medicare insurance. Methods We conducted a single-center, retrospective review of patients operated between January 2017 and March 2018. Knee Disability and Osteoarthritis Outcome Score Junior (KOOS-Jr) and Veterans RAND 12 Health Survey (VR-12) Physical Component (VR-12 PCS) and Mental Component (VR-12 MCS) PRO scores were collected prospectively at baseline and 12 weeks postoperatively via an electronic patient rehabilitation application. Univariable and multivariable linear regressions were utilized to assess the effects of patient insurance type on PRO. Results In total, 193 TKA candidates had commercial (n = 91) or Medicare (n = 102) as their primary payer type. Demographic variables including age, gender, body mass index, and race varied significantly between the cohorts ( P < .05). Length of stay and discharge disposition also varied significantly ( P < .05). When compared with commercial payers, Medicare beneficiaries demonstrated a 4.13 ± 2.06 increase in Knee Disability and Osteoarthritis Outcome Score JR. scores at baseline ( P < .05). However, after adjusting for patient-specific demographic and perioperative variables, all PROs recorded in this study were similar between the 2 payer groups at baseline and 12 weeks postoperatively ( P > .05). Furthermore, ΔPRO scores from baseline to 12 weeks were also similar ( P > .05). Conclusions After adjusting for patient-specific variables, PROs are similar at baseline and 12 weeks postoperatively between commercial and Medicare cohorts. For TKA candidates with similar baseline demographics, surgeons can expect similar perioperative PROs regardless of insurance type.

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Patient Adoption and Utilization of a Web-Based and Mobile-Based Portal for Collecting Outcomes After Elective Orthopedic Surgery

Cited by 19 publications

References 22 publications

Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis

Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

Payer type does not impact patient-reported outcomes after primary total knee arthroplasty

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